Dr Mark Green

Metrics play a crucial role in evaluating the performance of machine learning (ML) models. Metrics for quantifying the extent of errors, in particular, have been intensively studied and widely used but only so far for regression models. This paper focuses instead on classifier models. A new approach is proposed in which datamorphic exploratory testing is used to discover the boundary values between classes and the distance of misclassified instances from that boundary is used to quantify the errors that the model makes. Empirical experiments and case studies are reported that validate and evaluate the proposed metrics.

The microservices architecture is widely regarded as a promising approach to service-oriented systems. However, developing applications in the microservices architecture presents three main challenges: (a) how to program systems that consists of a large number of services running in paral- lel and distributed over a cluster of computers; (b) how to reduce the communication overhead caused by executing a large number of small services; (c) how to support the flexi- ble deployment of services to a network to achieve system load balance. This paper presents a programming language called CAOPLE and reports the implementation of the lan- guage on a virtual machine called CAVM-2. The paper demonstrates how this approach meets these challenges.